UNIT-3
Knowledge Based Agents:
The representation of knowledge and the reasoning processes that bring knowledge to
life-are central to the entire field of artificial intelligence. Humans, it seems, know things
and do reasoning. Knowledge and reasoning are also important for artificial agents
because they enable successful behaviors that would be very hard to achieve otherwise.
We have seen that knowledge of action outcomes enables problem solving agents to
perform well in complex environments. A reflex agent could only find its way from Arad
to Bucharest by dumb luck.
The knowledge of problem-solving agents is however, very specific and inflexible. A
chess program can calculate the legal moves of its king, but does not know in any
useful sense that no piece can be on two different squares at the same time. Knowledge-
based agents can benefit from knowledge expressed in very general forms,
combiningand recombining information to suit myriad purposes. Often, this process can
be quite far removed from the needs of the moment-as when a mathematician proves a
theorem or an astronomer calculatesthe earth's life expectancy.
Knowledge and reasoning also play a crucial role in dealing with partially observable
environments. A knowledge-based agent can combine general knowledge with current
percepts to infer hidden aspects of the current state prior to selecting actions. For example,
a physician diagnoses a patient-that is, infers a disease state that is not directly
observable prior to choosing a treatment.
Some of the knowledge that the physician uses is in the form of rules learned from
textbooks and teachers, and some is in the form of patterns of association that the
physician may not be able to consciously describe. If it's inside the physician's head, it
counts as knowledge.
Understanding natural language also requires inferring hidden state, namely, the
intention of the speaker. When we hear, "John saw the diamond through the window
and coveted it," we know "it" refers to the diamond and not the window-we reason,
perhaps unconsciously, with our knowledge of relative value. Similarly, when we
hear, "John threw the brick through the window and broke it," we know "it" refersto
the window.
Reasoning allows us to cope with the virtually infinite variety of utterances using a finite
store of commonsense knowledge. Problem- solving agents have difficulty with this
kind of ambiguity because their representation of contingency problems is inherently
exponential. Our final reason for studying knowledge-based agents is their flexibility.
They are able to accept new tasks in the form of explicitly described goals, they can
achieve competence quickly by being told or learning new knowledge about the
environment, and they can adapt to changes in the environment by updating the
relevant knowledge.